Parking areas such as parking garages and parking lots are used at various locations, ranging from shopping malls to airports. A particular aspect of parking areas involves off-street parking and the management of parking users, costs, and revenue. In a number of off-street parking situations, it is desirable to optimize a given characteristic of measure from an off-street parking area. Such a characteristic may be, for example, the overall revenue of the parking area or lot. Currently, all that is available is historical data from the parking lot or area itself, such as records of user arrival and leave times, in addition to some information about the parking lot infrastructure. It would be desirable to know in advance what would happen if a parking area (e.g., a parking zone, parking lot, etc.) is open, closed, or some of its characteristics (e.g., current price rates, average time distance from points of interest) have changed.
Several highly specialized approaches have been proposed to model parking user demand. One model, for example, is an agent-based system that can simulate parking and traffic situations under different parking management conditions in the context of an entire city. In this system, the number of agents operating inside the city was roughly estimated from a travel survey and was further tuned by running the model several times after gradually reducing the number of agents at each time. The parking demand was specified for different places by considering different attraction factors per trip motive (e.g., recreation, work, shopping) for each kind of budding (e.g., restaurant, residential or office). Afterwards, the calibration process was performed by asking field experts of the city administration to assess results based on their knowledge.
Simulation models for parking systems have also been proposed. However, when confronted with the specific problem of simulating duration time (e.g., the time a car stays parked in the parking lot), such models have adopted an average duration time for all vehicles within a certain period. Another approach that has been discussed involves designing a parking search model based on a utility maximization theory. This approach, however, is centered on on-street parking or parking lots spread throughout a city. It is not based on learning the user behavior nor on user profiles.
Another related work involves the use of MISIM (a microscopic traffic simulator developed at MIT) to simulate off-street parking and investigate how to create a user-choice model for this task. However, in this work, the author chose to define behavior groups a priori, dividing users into fixed, guided and un-guided. Moreover, a manually-crafted algorithm was used in this case to determine whether a user would chose to park or queue for a new option. In our system, the groups are determined automatically by an unsupervised algorithm. Furthermore, in our system the choice behaviors of each group are learned using a supervised algorithm from the historical data, instead of being manually and rigidly programmed into a computer. Other demand strategies include a regression model, which attempts to estimate parking demand as a parametric formula whose general form has to be specified manually or picked among candidates.